Knowledge-Based Energy Functions for Computational Studies of Proteins
This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design.We discuss in some detail the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and nonlinear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.
KeywordsPotential Function Protein Structure Prediction Residue Type Residue Pair Cargo Protein
Unable to display preview. Download preview PDF.
- Berkelaar, M. 2004. LP_Solve package. URL http://www.cs.sunysb.edu/algorith/implement/lpsolve/implement.shtmlGoogle Scholar
- Burges, C.J.C. 1998. A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining 2. URL /papers/Burges98.ps.gzGoogle Scholar
- Czyzyk, J., Mehrotra, S., Wagner, M., and Wright, S. 2004. PCx package. URL http://www-fp.mcs.anl.gov/otc/Tools/PCx/Google Scholar
- Gilis, D. 2004. Protein decoy sets for evaluating energy functions. J. Biomol. Struct. Dyn. 21:725–736.Google Scholar
- Li, X., and Liang, J. 2005a. Computational design of combinatorial peptide library for modulating protein—protein interactions. Pacific Symposium of Biocomputing.Google Scholar
- Momany, F.A., McGuire, R.F., Burgess, A.W., and Scheraga, H.A. 1975. Energy parameters in polypeptides. VII. Geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids. J. Phys. Chem. 79:2361–2381.CrossRefGoogle Scholar
- Schölkopf, B., and Smola, A.J. 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, MIT Press.Google Scholar
- Vapnik, V., and Chervonenkis, A. 1964. A note on one class of perceptrons. Automation and Remote Control 25.Google Scholar
- Vapnik, V., and Chervonenkis, A. 1974. Theory of Pattern Recognition [in Russian]. Nauka, Moscow, (German Translation: W. Wapnik & A. Tscherwonenkis, Theorie der Zeichenerkennung, Akademie—Verlag, Berlin, 1979).Google Scholar
- Zheng, W., Cho, S.J., Vaisman, I.I., and Tropsha, A. 1997. A new approach to protein fold recognition based on Delaunay tessellation of protein structure. Pac. Symp. Biocomput. pp. 486–497.Google Scholar